[go: up one dir, main page]

EP4264542A1 - Procédé d'analyse d'image pour prise de décision clinique améliorée - Google Patents

Procédé d'analyse d'image pour prise de décision clinique améliorée

Info

Publication number
EP4264542A1
EP4264542A1 EP21847677.8A EP21847677A EP4264542A1 EP 4264542 A1 EP4264542 A1 EP 4264542A1 EP 21847677 A EP21847677 A EP 21847677A EP 4264542 A1 EP4264542 A1 EP 4264542A1
Authority
EP
European Patent Office
Prior art keywords
image
shape
image shape
modified
analysis method
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP21847677.8A
Other languages
German (de)
English (en)
Inventor
Wim VOS
Sean Walsh
Ralph T.H. LEIJENAAR
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Oncoradiomics Sa
Original Assignee
Oncoradiomics Sa
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Oncoradiomics Sa filed Critical Oncoradiomics Sa
Publication of EP4264542A1 publication Critical patent/EP4264542A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformations in the plane of the image
    • G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
    • G06V10/806—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of extracted features
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20036—Morphological image processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion

Definitions

  • the geometrical deformation multiplier c is selected based on the two-dimensional or three-dimensional geometrical shape most closely fitting to the image shape A.
  • the physiological deformation multiplier d may be selected based on the shape and size of an organ or parts thereof.
  • the image I is an ultrasound image.
  • the imaging shape A by shifting the imaging shape A it is meant to drag the image shape A in any direction in the image I so as to obtain a modified image shape Ba having the same proportions of the image shape A and having a different position compared to the position of the image shape A. Therefore, the image shape A and the modified image shape Ba are congruent.
  • the modification step 300 comprises: detecting in the image I a physiological object; determining the shape of said physiological object; for each pixel of the image shape A, calculating a physiological deformation multiplier d based on the shape of the physiological object; multiplying the position of each pixel of the image shape A for the physiological deformation multiplier d, so as to obtaining a modified image shape Bd.
  • the modification step 300 comprises a modification such as rotation, reflection, affine transformation, polynomial transformation, or piecewise linear transformation.
  • a predictive value is derived by a processing unit based on the image feature parameters.
  • the value derived in derivation step 600 is a predictive value, a diagnostic value, a therapeutic value, a prognostic value or a theragnostic value.
  • a selection step 500 is performed to select a subset of image feature parameters, and the predictive value is derived based on said subset of image feature parameters. Selecting a subset of image feature parameters allows to minimize the error classification of the predictive model.
  • Tumor histological subtype is one of the main clinical aspects that may influence treatment decision making for non-small cell lung cancer (NSCLC) patients.
  • NSCLC non-small cell lung cancer
  • the present invention is applied to evaluate the performance of a machine learning model to classify the squamous cell carcinoma (SCC) histological subtype of NSCLC.
  • SCC squamous cell carcinoma
  • the predictive value is derived by means of a machine learning algorithm.

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Public Health (AREA)
  • Epidemiology (AREA)
  • Biomedical Technology (AREA)
  • Databases & Information Systems (AREA)
  • Primary Health Care (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Data Mining & Analysis (AREA)
  • Pathology (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Quality & Reliability (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

La présente invention concerne un procédé d'analyse d'image permettant de fournir des informations pour une prise en charge à des fins diagnostiques, thérapeutiques, pronostiques ou théragnostiques de corps humains et animaux. La présente invention concerne également un système pour effectuer des tâches d'analyse sur la base d'images permettant de fournir des informations pour une prise en charge à des fins diagnostiques, thérapeutiques, pronostiques ou théragnostiques de corps humains et animaux.
EP21847677.8A 2020-12-18 2021-12-06 Procédé d'analyse d'image pour prise de décision clinique améliorée Withdrawn EP4264542A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP20215700.4A EP4016448A1 (fr) 2020-12-18 2020-12-18 Procédé d'analyse d'image pour améliorer la prise de décision clinique
PCT/EP2021/084459 WO2022128587A1 (fr) 2020-12-18 2021-12-06 Procédé d'analyse d'image pour prise de décision clinique améliorée

Publications (1)

Publication Number Publication Date
EP4264542A1 true EP4264542A1 (fr) 2023-10-25

Family

ID=73855927

Family Applications (2)

Application Number Title Priority Date Filing Date
EP20215700.4A Withdrawn EP4016448A1 (fr) 2020-12-18 2020-12-18 Procédé d'analyse d'image pour améliorer la prise de décision clinique
EP21847677.8A Withdrawn EP4264542A1 (fr) 2020-12-18 2021-12-06 Procédé d'analyse d'image pour prise de décision clinique améliorée

Family Applications Before (1)

Application Number Title Priority Date Filing Date
EP20215700.4A Withdrawn EP4016448A1 (fr) 2020-12-18 2020-12-18 Procédé d'analyse d'image pour améliorer la prise de décision clinique

Country Status (4)

Country Link
US (1) US20240055124A1 (fr)
EP (2) EP4016448A1 (fr)
BE (1) BE1028729B1 (fr)
WO (1) WO2022128587A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN119667105B (zh) * 2025-02-20 2025-05-13 洛阳理工学院 一种钢丝绳表面损伤智能识别及直径测量同步检测系统

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6289142B2 (ja) * 2014-02-07 2018-03-07 キヤノン株式会社 画像処理装置、画像処理方法、プログラムおよび記憶媒体
US11284851B2 (en) * 2018-03-23 2022-03-29 Case Western Reserve University Differential atlas for identifying sites of recurrence (DISRN) in predicting atrial fibrillation recurrence
US11158045B2 (en) * 2018-10-10 2021-10-26 David Byron Douglas Method and apparatus for performing 3D imaging examinations of a structure under differing configurations and analyzing morphologic changes
EP3925514B1 (fr) * 2019-02-14 2024-06-19 NEC Corporation Dispositif de division de zone de lésion, système de diagnostic d'image médicale, procédé de division de zone de lésion, et support lisible par ordinateur non transitoire de stockage de programme
CN115996670B (zh) * 2020-05-13 2025-10-31 Eos成像公司 医学成像转换方法和相关联的医学成像3d模型个性化方法

Also Published As

Publication number Publication date
WO2022128587A1 (fr) 2022-06-23
US20240055124A1 (en) 2024-02-15
EP4016448A1 (fr) 2022-06-22
BE1028729B1 (fr) 2022-05-18

Similar Documents

Publication Publication Date Title
US10134141B2 (en) System and methods for image segmentation using convolutional neural network
US9968257B1 (en) Volumetric quantification of cardiovascular structures from medical imaging
AU2017315674B2 (en) Image segmentation using neural network method
Chaddad et al. Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images
Lu et al. Fully automatic liver segmentation combining multi-dimensional graph cut with shape information in 3D CT images
Häme et al. Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation
US20110158491A1 (en) Method and system for lesion segmentation
CN110050281A (zh) 学习图像中的对象的注释
CN110910405A (zh) 基于多尺度空洞卷积神经网络的脑肿瘤分割方法及系统
US11182901B2 (en) Change detection in medical images
US20200273167A1 (en) Assessment of arterial calcifications
Rani et al. Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification
KR20240115234A (ko) 의료 영상 내 생물학적 객체의 머신 러닝 기반 세그먼트화
Hasan A hybrid approach of using particle swarm optimization and volumetric active contour without edge for segmenting brain tumors in MRI scan
CN117649400B (zh) 一种异常检测框架下的影像组学分析方法及系统
Nadeem et al. Automated CAD system for early detection and classification of pancreatic cancer using deep learning model
Gupta et al. Methods for increased sensitivity and scope in automatic segmentation and detection of lung nodules in CT images
US20240055124A1 (en) Image analysis method for improved clinical decision making
CN114693671A (zh) 基于深度学习的肺结节半自动分割方法、装置、设备及介质
Reska et al. GPU-accelerated lung CT segmentation based on level sets and texture analysis
Egger et al. Interactive outlining of pancreatic cancer liver metastases in ultrasound images
Kubicek et al. Autonomous segmentation and modeling of brain pathological findings based on iterative segmentation from MR images
Park et al. Artificial intelligence in radiology and its application in liver disease
Khan A hybrid unsupervised classification technique for automatic brain MRI tumor recognition
Kazi Image Processing in Biomedical Research

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20230530

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAV Request for validation of the european patent (deleted)
DAX Request for extension of the european patent (deleted)
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20240206